Search Results (188)

Hazelnut (Corylus avellana L.) is a major crop in the Caucasus region, but its safety is often threatened by Aspergillus flavus colonization and aflatoxin (AF) contamination. Although AFs are strictly regulated in the EU, the influence of post-harvest practices on fungal persistence and AF accumulation remains poorly defined. A three-year study was conducted to evaluate the effects of drying protocols, storage temperature, and conservation practices on fungal growth and AF occurrence in hazelnuts from three producing regions of Azerbaijan. Freshly harvested nuts were subjected to two drying regimes: good drying (sun-exposed, mixed, protected from rewetting) and bad drying (shaded, piled, rewetted). After drying, samples were stored at cold (8–10 °C) or room temperature (18–22 °C). Fungal prevalence was determined by CFU counts with morphological and qPCR identification of Aspergillus section Flavi. AFs were quantified by HPLC, and water activity (a_w) was monitored during storage. Drying emerged as the decisive factor: bad drying consistently resulted in markedly higher fungal loads for A. section Flavi, with mean counts up to 1.5 × 10² CFU/g, compared with 2.1 × 10¹ CFU/g under good drying, representing a 7-fold increase. In contrast, storage temperature and shell condition had negligible effects when nuts were properly dried. Aflatoxins were consistently below the 5 µg/kg EU limit for AFB₁ in traced and well-dried samples, whereas market samples occasionally exhibited AFB₁ concentrations >450 µg/kg. These findings highlight drying efficiency as the key determinant of fungal persistence and AF risk in hazelnut post-harvest management. Full article

Against the backdrop of global warming and a reshaped hydrothermal regime, the albic soil belt of the Sanjiang Plain, a major grain base, requires farm-scale evidence of how meteorological variability couples with staple-crop yields. Using meteorological and yield records from 2000 to 2023 at three large farms (859, 850, and 852), this study applied the Mann–Kendall test, wavelet and cross-wavelet coherence, Pearson correlation, gray relational analysis, and principal component analysis to track the evolution of air temperature, precipitation, evaporation, sunshine duration, relative humidity, and surface temperature, and to assess their multi-scale impacts on rice, corn, and soybean yields. The region warmed and became wetter overall, with dominant periodicities near 21a and 8a. Across the three farms, yields were significantly and positively associated with precipitation and air temperature (R > 0.60). Rice yield correlated strongly and negatively with evaporation at Farm 850 (R = −0.61) and at Farm 852 (R = −0.503). At Farm 859, gray relational analysis ranked precipitation highest for rice, corn, and soybean (γ = 0.853, 0.844, and 0.826), followed by air temperature. The first two principal components explained 67.66% of the variance; PC1 (41.80%) loaded positively for air temperature, and PC2 (25.86%) for precipitation and relative humidity. Cross-wavelet coherence indicated stable coupling between yields and hydrothermal variables, with the strongest coupling for rice with precipitation and air temperature, prominent coupling for corn with air temperature and sunshine duration, and stage-dependent responses of soybean to precipitation and evaporation. These results show that long-term trends together with phase-specific oscillations jointly shape yield variability. The findings support translating phase identification and sensitive windows into crop-specific rules for sowing or transplanting arrangements, irrigation timing, and early warning, providing a quantitative basis for climate-adaptive management on the study farms and, where soils, management, and microclimate are comparable, for the wider Sanjiang Plain. Full article

This study investigated the site-specific ionic composition and wet deposition loads of rainwater collected from eight actively cultivated agricultural regions across South Korea, with the aim of quantifying spatial and seasonal variability and interpreting how regional agricultural characteristics and surrounding site conditions influence major ion concentrations and deposition patterns. Rainfall samples were obtained using automated samplers and analyzed via high-performance ion chromatography for major cations (Na⁺, $\mathrm{N}{\mathrm{H}}_4^{+}$, K⁺, Ca²⁺, Mg²⁺) and anions (Cl⁻, $\mathrm{N}{\mathrm{O}}_3^{-}$, $\mathrm{S}{\mathrm{O}}_4^{2-}$, $\mathrm{N}{\mathrm{O}}_2^{-}$). The results revealed significant seasonal fluctuations in ion loads, with $\mathrm{N}{\mathrm{H}}_4^{+}$ (peak 1.13 kg/ha) and K⁺ (peak 0.25 kg/ha) reaching their highest levels during summer due to increased fertilizer use and crop activity. Conversely, Cl⁻ peaked in winter (2.11 kg/ha in December), particularly in coastal regions, likely influenced by de-icing salts and sea-salt aerosols. Correlation analysis showed a strong positive association among $\mathrm{N}{\mathrm{H}}_4^{+}$, $\mathrm{N}{\mathrm{O}}_3^{-}$, and $\mathrm{S}{\mathrm{O}}_4^{2-}$ (r = 0.89 and r = 0.84, respectively), indicating shared atmospheric transformation pathways from agricultural emissions. Ternary diagram analysis further revealed regional distinctions: coastal regions such as Gimhae and Muan exhibited Na⁺ and Cl⁻ dominance, while inland areas like Danyang and Hongcheon showed higher proportions of Ca²⁺ and Mg²⁺, reflecting differences in aerosol sources, land use, and local meteorological conditions. These findings underscore the complex interactions between agricultural practices, atmospheric processes, and local geography in shaping rainwater chemistry. The study provides quantitative baseline data for evaluating non-point source pollution and developing region-specific nutrient and soil management strategies in agricultural ecosystems. Full article

This review relates the kinetics of anaerobic digestion (AD) to energy outcomes, including typical ranges of methane yields and volumetric methane productivities (down to hourly g L⁻¹ h⁻¹ scales relevant for industrial plants). It further translates these relationships into practical control principles that support stable, high methane productivity. Evidence spans substrate selection and co-digestion with emphasis on carbon/nitrogen (C/N) balance, pretreatment strategies, and reactor operation, linking process constraints with operating parameters to identify interventions that raise performance while limiting inhibition. Improving substrate accessibility is the primary step: pretreatment and co-digestion shift limitation beyond hydrolysis and allow safe increases in organic loading. Typical mesophilic operation involves hydraulic retention times of about 10–40 days for food waste and 20–60 days for different types of livestock manure and slowly degradable energy crops, with stable performance achieved when the solids retention time (SRT) is maintained longer than the hydraulic retention time (HRT). Stability is further governed by sustaining a low hydrogen partial pressure through hydrogenotrophic methanogenesis. Temperature and pH define practicable operating ranges; meanwhile, mixing should minimise diffusion resistance without damaging biomass structure. Early-warning indicators—volatile fatty acids (VFAs)/alkalinity, the propionate/acetate ratio, specific methanogenic activity, methane (CH₄)% and gas flow—enable timely adjustment of loading, retention, buffering, mixing intensity and micronutrient supply (Ni, Co, Fe, Mo). In practice, robust operation is generally associated with VFA/alkalinity ratios below about 0.3 and CH₄ contents typically in the range of 50–70% (v/v) in biogas. The review consolidates typical feedstock characteristics and biochemical methane potential (BMP) ranges, as well as outlines common reactor types with their advantages and limitations, linking operational choices to energy yield in combined heat and power (CHP) and biomethane pathways. Reported pretreatment effects span approximately 20–100% higher methane yields; for example, 18–37% increases after mechanical size reduction, around 20–30% gains at 120–121 °C for thermal treatments, and in some cases nearly a two-fold increase for more severe thermal or combined methods. Priorities are set for adaptive control, micronutrient management, biomass-retention strategies, and standardised monitoring, providing a coherent route from kinetic understanding to dependable energy performance and explaining how substrate composition, pretreatment, operating parameters, and kinetic constraints jointly determine methane and energy yield, with particular emphasis on early-warning indicators. Full article

Conventional agricultural practices, based on intensive irrigation and heavy fertilizer and pesticide inputs, are increasingly incompatible with climate change, soil degradation, and sustainability goals. Hydrogels have emerged as promising soil amendments to improve water and nutrient management, and fall broadly into two categories: synthetic polyacrylate/polyacrylamide-based systems and natural biobased hydrogels derived from polysaccharides such as alginate, cellulose, and chitosan. The latter, often obtained from agro-industrial residues, offer biodegradable and potentially lower-impact alternatives to persistent synthetic matrices. This review analyzes recent advances in the design and application of nanocomposite hydrogels in agricultural crops, with emphasis on high-value systems such as tomato, chili pepper and maize. Representative studies show that hydrogel–nanofertilizer formulations can increase soil water retention in tomato from ~55–56% to ~78–79%, nearly double swelling capacity in wheat, reduce irrigation requirements by around 15% in legumes, and improve plant biomass by ~30–40% under drought conditions. In parallel, nanocomposite hydrogels loaded with micronutrients, phytochemicals or biostimulants can enhance nutrient uptake, provide 36–80% protection against Fusarium wilt, and reduce postharvest pathogen growth by up to ~90%, while in some cases improving the nutraceutical quality of fruits. These outcomes illustrate a dual mechanism of action in which the hydrogel matrix acts as a micro-reservoir that buffers water and nutrients, whereas nano- and phytochemical components operate as physiological eustressors that modulate plant defense and metabolism. Finally, we discuss environmental and translational challenges, including hydrogel biodegradation pathways, the long-term fate and ecotoxicity of released nanoparticles, regulatory uncertainty, and market and field acceptance. Addressing these gaps through integrative agronomic, ecotoxicological, and regulatory studies is essential to ensure that nanocomposite hydrogels evolve into truly sustainable smart carriers for fertilizers, pesticides, and biostimulants in future cropping systems. Full article

After the non-point source pollutants are generated at the source position and migrate to the target water body, they will have different degrees of loss under the action of precipitation, adsorption, or absorption by plants, resulting in differences in pollution output load and generation amount. Taking the Xin’an River Basin as an example, this study analyzes the spatial distribution characteristics of non-point source pollution generation and output in the process of river migration and explores the influence of river migration on non-point source pollution based on the soil and water assessment tool (SWAT) model and mathematical statistical methods. The results showed that the spatial distribution intensity of total nitrogen and total phosphorus in different sub-basins of Xin’an River Basin is between 3.88 and 29.16 kg/ha and 0.11–1.18 kg/ha, respectively. The high intensity areas of non-point source pollution generation and output are mainly concentrated in the hydrologically sensitive areas in the southern part of the basin and the erosion-sensitive area in the southeastern part of the basin, and the critical source areas of non-point source pollution are a result of comprehensive effects of crop fertilizer input, soil nitrogen, and phosphorus storage as well as hydrology and soil erosion. There are differences in the spatial distribution of non-point source pollution generation and output in the process of river migration. Some sub-basins have significant changes in their generation and output, and the sub-basin output coefficients of total nitrogen and total phosphorus are between 0.856 and 1.014 and 0.998–1.061, respectively. The change intensity of pollutants after river migration is affected by the combined effects of migration time, runoff intensity, material adsorption, and desorption, etc. The research findings will provide scientific support for zonal management and targeted measures of non-point source pollution in the Xin’an River Basin. Full article

Energy expenditure constitutes a significant portion of total operational costs in greenhouse crop production. Developing accurate energy consumption prediction models presents crucial theoretical foundations for optimizing the environmental control strategies aimed at energy efficiency enhancement. This study focuses on steel-frame solar greenhouses without back slopes in Xinjiang’s Tianshan North Slope region. A physical model was established using thermodynamic equilibrium analysis, elucidating the energy exchange mechanisms between internal and external environments. Key parameters, including outdoor temperature and solar radiation, were identified as primary input variables through systematic energy flow characterization. Building upon this theoretical framework, we developed an enhanced prediction model (WOA-ELM) by integrating the Whale Optimization Algorithm (WOA) with an Extreme Learning Machine (ELM). The WOA’s global optimization capabilities were employed to refine the connection weights between input-hidden layers and optimize hidden neuron thresholds. Comparative evaluations against conventional artificial neural networks (ANNs), radial basis function neural networks (RBFNN), and baseline ELM models were conducted under diverse meteorological conditions. Experimental results demonstrate the superior performance of WOA-ELM across multiple metrics. Under overcast conditions, the model achieved a root mean square error (RMSE) of 0.423, coefficient of determination (R²) of 0.93, and mean absolute error (MAE) of 0.252. In clear weather scenarios, performance further improved with RMSE = 0.27, R² = 0.96, and MAE = 0.063. The comprehensive evaluation ranked model effectiveness as WOA-ELM > ELM > BP > RBF. These findings substantiate that the hybrid WOA-ELM architecture, combining physical mechanism interpretation with intelligent parameter optimization, delivers enhanced prediction accuracy across varying weather patterns. This research provides valuable insights for energy load management in backslope-less steel-frame greenhouses, offering theoretical guidance for thermal environment regulation and sustainable operation. Full article

Traditional environmental research remains affixed in fragmented metrics (e.g., CO₂ emissions or ecological footprints) that undermine the systemic equilibrium between economic demand and ecological regeneration. Biocapacity, representing the capacity of lands (crop and grazing), forests, and other natural systems, is the backbone of economic livelihoods and environmental resilience. Recent literature frequently calls for operationalizing models with robust environmental sustainability indicators, such as the load capacity factor (LF), a comprehensive compass that measures biocapacity (e.g., forests, croplands) relative to ecological footprint. For this purpose, the integrated model combined environment-related policies (regulations, ENRs), technologies (ERTs), sectoral structures, and LF, with the latest available data (2000–2022) of G20 economies. Results of the multiple tests, including feasible generalized least squares, sensitivity tests (alternate proxies), and panel-corrected standard errors, highlighted a paradox: even though ENRs and ERTs tend to improve environmental sustainability through forestation, land use, and green initiatives, the results showed adverse effects of both indicators on environmental sustainability (LF), reflecting a misalignment between policies and environmental outcomes. While industrialization, renewable energy use, and rising per capita income had enhanced environmental sustainability (LF) gains, structural frictions in the services, manufacturing, and trade sectors undermined these advantages, revealing diffusion lags and transitional lock-ins across sampled countries. With LF embedded as a new tool for sustainable governance of forests and land management, the paper advances three critical contributions: (i) uncovering paradoxical deteriorations in sustainability under misaligned policy and technology interventions, (ii) showing an imperative need for performance-based, adaptive, and innovation-financed policies, and (iii) demonstrating LF as a standard for positioning technology, economic transitions, and policy with ecological and cropland-forests resilience. Full article

Manure-based anaerobic digestion (AD) systems serve multiple functions, including waste treatment, energy recovery, and nutrient cycling. However, they also entail additional energy consumption and pollutant emissions. Life cycle assessment (LCA) methodology is typically used to holistically quantify the actual environmental impacts of these systems. Nevertheless, comprehensive reviews synthesizing LCA studies in this field remain limited. Following PRISMA guidelines, this study conducted a systematic literature review of LCA studies on manure-based AD systems, focusing on advancements, inconsistencies, and limitations in LCA methodologies and environmental impact results. The findings indicate considerable variability in functional units, allocation methods, system boundaries, and inventory analysis methods across the literature. These methodological discrepancies and the lack of standardized protocols result in remarkable variability in environmental impact potentials. Additionally, there is lack of consensus on the environmental benefits of AD systems compared to traditional manure management, and co-digestion with energy crops or food waste compared to mono-digestion of manure. Consequently, the environmental impacts of manure-based AD systems remain inconclusive due to methodological heterogeneity and data inconsistencies. Future research should develop scientific and standardized approaches and focus on the completeness of system boundaries, selection of key environmental impact categories, environmental load allocation, inventory data quality, and the transparency of the analysis. Full article

Nitrogen (N) and phosphorus (P) play vital roles in crop growth. However, conventional fertilizers exhibit low utilization efficiency, making them prone to causing resource wastage and water eutrophication. Although metal–organic frameworks (MOFs) have shown great potential for application in controlled-release fertilizers (CRFs), currently reported MOF-based CRFs suffer from low nutrient content, which limits their further application. To address this issue, this study synthesized a series of hierarchically porous MOFs, denoted as MIL-156(X), using sodium acetate as a modulator under hydrothermal conditions. These materials were subsequently loaded with urea and phosphate from aqueous solution to form MOFs-based CRFs (N-P-MIL-156(X)). Results indicate that MIL-156(X) retain microporous integrity while incorporating abundant mesopores. Increasing modulator content reduced particle size and average pore diameter but increased specific surface area and adsorption capacity for urea and phosphate. MIL-156-H (with a high modulator content addition) exhibited the highest adsorption capacity, conforming to Langmuir isotherm and pseudo-second-order kinetics. The adsorption mechanisms of urea and phosphate involved hydrogen bonding and the formation of Ca intra-spherical complexes, respectively. N-P-MIL-156-H contained 10.8% N and 16.3% P₂O₅, with sustained release durations exceeding 42 days (N) and 56 days (P₂O₅) in an aqueous solution. Pot trials demonstrated significantly higher nutrient use efficiency (N-44.8%, P₂O₅-16.56%) and a 12.22% yield increase compared to conventional fertilization (N-35.6%, P₂O₅-13.32%). Thus, N-P-MIL-156-H-based fertilization significantly promotes rice growth and N/P utilization efficiency, offering a promising strategy for developing controlled-release fertilizers and improving nutrient management. Full article

This study presents the design and implementation of a crop environmental monitoring system powered by a plant–soil bioelectrochemical energy source. The system integrates a Cu–Zn electrode power unit, a boost converter, a supercapacitor-based energy management module, and a wireless sensing node for real-time monitoring of environmental parameters. Unlike conventional plant microbial fuel cells (PMFCs), the output current originates partly from the galvanic effect of Cu–Zn electrodes and is further regulated by rhizosphere conditions and microbial activity. Under the optimal external load (900 Ω), the system achieved a maximum output power of 0.477 mW, corresponding to a power density of 0.304 mW·cm⁻². Stability tests showed that with the boost converter and supercapacitor, the system maintained a stable operating voltage sufficient to power the sensing node. Soil moisture strongly influenced performance, with higher water content increasing power by about 35%. Theoretical calculations indicated that Zn corrosion alone would limit the anode lifetime to ~66 days; however, stable output during the experimental period suggests contributions from plant–microbe interactions. Overall, this work demonstrates a feasible self-powered crop monitoring system and provides new evidence for the potential of plant–soil bioelectrochemical power sources in low-power applications. Full article

Soil compaction is a pivotal factor influencing crop growth and yield, and its accurate assessment is imperative for precision agricultural management. Soil mechanical resistance is the key indicator of soil compaction, with accurate measurement enabling precise assessment. Dynamic soil mechanical resistance measurement outperforms conventional manual fixed-point sampling in data acquisition efficiency. In this paper, a methodology is proposed for the dynamic acquisition of soil mechanical resistance using a flexible thin-film pressure sensor. This study dynamically captures soil mechanical resistance at three depths (5 cm, 10 cm, and 15 cm) under dynamic machinery operating conditions. A device was designed for the detection of soil mechanical resistance, and a prediction model for soil mechanical resistance was developed based on the Kalman filter algorithm. Tests were conducted under steady-state and variable-load conditions, and the predicted values accurately tracked the reference pressure. Soil tank trials showed that at an operating speed of 0.69–0.72 km/h, the average prediction errors for the three soil layers were 2.03%, 1.48%, and 6.27%, with the coefficient of determination (R²) between predicted and measured values reaching 0.96. The system effectively predicts multi-depth soil resistance, providing novel theoretical and technical approaches for dynamic acquisition. Full article

In the present work, six biochars were produced from the pyrolysis of poultry manure at 400 °C and 600 °C (PM-B-400 and PM-B-600), and their post-modification with, respectively, iron chloride (PM-B-400-Fe and PM-B-600-Fe) and potassium permanganate (PM-B-400-Mn and PM-B-600-Mn). First, these biochars were deeply characterized through the assessment of their particle size distribution, pH, electrical conductivity, pH at point-zero charge, mineral composition, morphological structure, and surface functionality and crystallinity, and then valorized as biofertilizer to grow spring barley at pot-scale for 40 days. Characterization results showed that Fe- and Mn-based nanoparticles were successfully loaded onto the surface of the post-modified biochars, which significantly enhanced their structural and surface chemical properties. Moreover, compared to the control treatment, both raw and post-modified biochars significantly improved the growth parameters of spring barley plants (shoot and root length, biomass weight, and nutrient content). The highest biomass production was obtained for the treatment with PM-B-400-Fe, owing to its enhanced physico-chemical properties and its higher ability in releasing nutrients and immobilizing heavy metals. These results highlight the potential use of Fe-modified poultry manure-derived biochar produced at low temperatures as a sustainable biofertilizer for soil enhancement and crop yield improvement, while addressing manure management issues. Full article

Conventional tillage and monocropping are common practices employed for cotton production in the Southern Great Plains (SGP) region, but they can be detrimental to soil health, crop yield, and water resources when improperly managed. Regenerative practices such as cover crops and conservation tillage have been suggested as an alternative. The proposed shift in management practices originates from the need to make agriculture resilient to extreme weather events including intense rainfall and drought. The objective of this study is to test the effects of these regenerative practices in an environment with limited rainfall. Runoff volume, nutrient and sediment concentrations and loadings, and surface soil moisture levels were compared on twelve half-acre (0.2 hectare) cotton plots that employed different cotton seeding rates and variable winter wheat cover crop presence. A winter cover implemented on plots with a high cotton seeding rate significantly reduced runoff when compared to other treatments (p = 0.032). Cover cropped treatments did not show significant effects on nutrient or sediment loadings, although slight reductions were observed in the concentrations and loadings of total Kjeldahl nitrogen, total phosphorus, total solids, and Escherichia coli. The limitations of this study included a short timeframe, mechanical failures, and drought. These factors potentially reduced the statistical differences in several findings. More efficient methods of crop production must continue to be developed for agriculture in the SGP to conserve soil and water resources, improve soil health and crop yields, and enhance resiliency to climate change. Full article

To address the challenges of low recognition accuracy and substantial model complexity in crop disease identification models operating in complex field environments, this study proposed a novel hybrid model named ConvTransNet-S, which integrates Convolutional Neural Networks (CNNs) and transformers for crop disease identification tasks. Unlike existing hybrid approaches, ConvTransNet-S uniquely introduces three key innovations: First, a Local Perception Unit (LPU) and Lightweight Multi-Head Self-Attention (LMHSA) modules were introduced to synergistically enhance the extraction of fine-grained plant disease details and model global dependency relationships, respectively. Second, an Inverted Residual Feed-Forward Network (IRFFN) was employed to optimize the feature propagation path, thereby enhancing the model’s robustness against interferences such as lighting variations and leaf occlusions. This novel combination of a LPU, LMHSA, and an IRFFN achieves a dynamic equilibrium between local texture perception and global context modeling—effectively resolving the trade-offs inherent in standalone CNNs or transformers. Finally, through a phased architecture design, efficient fusion of multi-scale disease features is achieved, which enhances feature discriminability while reducing model complexity. The experimental results indicated that ConvTransNet-S achieved a recognition accuracy of 98.85% on the PlantVillage public dataset. This model operates with only 25.14 million parameters, a computational load of 3.762 GFLOPs, and an inference time of 7.56 ms. Testing on a self-built in-field complex scene dataset comprising 10,441 images revealed that ConvTransNet-S achieved an accuracy of 88.53%, which represents improvements of 14.22%, 2.75%, and 0.34% over EfficientNetV2, Vision Transformer, and Swin Transformer, respectively. Furthermore, the ConvTransNet-S model achieved up to 14.22% higher disease recognition accuracy under complex background conditions while reducing the parameter count by 46.8%. This confirms that its unique multi-scale feature mechanism can effectively distinguish disease from background features, providing a novel technical approach for disease diagnosis in complex agricultural scenarios and demonstrating significant application value for intelligent agricultural management. Full article